Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Statistical power for identifying nucleotide markers associated with quantitative traits in genome-wide association analysis using a mixed model.

Genomics - 1 Jan 2015

Shin Jimin, Lee Chaeyoung

Abstract excerpt

Use of mixed models is in the spotlight as an emerging method for genome-wide association studies (GWASs). This study investigated the statistical power for identifying nucleotide variants associated with quantitative traits using the mixed model methodology. Quantitative traits were simulated through design of heritability, the number of causal variants (NCV), the number of polygenic variants, and genetic...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to C-Reactive Protein
Share to Computer Simulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome-Wide Association Study

Share to Models, Genetic

Share to Multifactorial Inheritance

Share to Nucleotides

Share to Quantitative Trait, Heritable

Source

Identifiers

PMID: 25451740
DOI: 10.1016/j.ygeno.2014.11.001

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Statistical power for identifying nucleotide markers associated with quantitative traits in genome-wide association analysis using a mixed model.PMID 25451740Read ↗

Underestimation of heritability using a mixed model with a polygenic covariance structure in a genome-wide association study for complex traits.Semantic proximity Read ↗

Optimizing genomic control in mixed model associations with binary diseases.Semantic proximity Read ↗

Genome-wide hierarchical mixed model association analysis.Semantic proximity Read ↗

A mixed model reduces spurious genetic associations produced by population stratification in genome-wide association studies.Semantic proximity Read ↗

Optimal Genomic Control in Large-scale Genetic Associations for Binary DiseasesSemantic proximity Read ↗

Methodological implementation of mixed linear models in multi-locus genome-wide association studies.Semantic proximity Read ↗

Analysis of nucleotide sequence data using mixed model methodology.Semantic proximity Read ↗

Trade-offs of Linear Mixed Models in Genome-Wide Association Studies.Semantic proximity Read ↗

Comparing mixed models and Random Forest association tests using naturalgwas and a Striped Bass SNP data set.Semantic proximity Read ↗

Using Monte Carlo method to include polygenic effects in calculation of SNP-BLUP model reliability.Semantic proximity Read ↗

A Shortcut Approach for Large-scale Mixed Model Associations with Binary TraitsSemantic proximity Read ↗

Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.